Day one of the first Cognitive Computing Forum in San Jose, hosted by Dataversity, gave a great perspective on the state of cognitive computing; promising, but early.  I am here this week with my research director Leslie Owens and analyst colleague Diego LoGudice.  Gathering research for a series of reports for our cognitive engagement coverage, we were able to debrief tonight on what we heard and the questions these insights raise.  Here are some key take-aways:

1)  Big data mind shift to explore and accept failure is a heightened principle.  Chris Welty, formerly at IBM and a key developer of Watson and it's Jeoapardy winning solution, preached restraint.  Analytic pursuit of perfect answers delivers no business value.  Keep your eye on the prize and move the needle on what matters, even if your batting average is only .300 (30%).  The objective is a holistic pursuit of optimization.

2)  The algorithms aren't new, the platform capabilities and greater access to data allow us to realize cognitive for production uses.  Every speaker from academic, vendor, and expert was in agreement that the algorithms created decades ago are the same.  Hardware and the volume of available data have made neural networks and other machine learning algorithms both possible and more effective.  

3)  You can seek consensus, or you get value from outliers.  There are two camps in cognitive computing beyond the typical dimensions of answers vs. discovery.  For example: one seeks to understand the propensity of an overall population to take particular action.  The other is optimizing algorithms to learn from anomalies.  Where outliers often are excluded in less sophisticated statistical analysis, they can bring new meaning and affects for businesses when they would otherwise be ignored with detrimental effects.

4) Algorithm wars are still a dominant factor of perceived cognitive computing effectiveness.  The number of academic insights and sessions on analytic techniques and algorithm explanations demonstrates the early nature of proposed vendor solutions.  Additionally, questions from event attendees often focused on validating analytic algorithms and recommended techniques.  Forrester looks forward to sessions lead and delivered by organizations running these capabilities in production in packages cognitive solutions newly being released by vendors as an indication that we are entering the true commercialization of what Forrester sees as cognitive engagement.

5) Shifting sands under the data scientist role.  The promise is that to harness cognitive capabilities, data scientists can be replaced by data curators and business analysts.  The value proposition is to allow business stakeholders to leverage the power of a cognitive system without the need to fully understand or program analytic algorithms and models. Instead, they directly bring their expertise and experience to source data for the systems, tune and improve the system based on guidance and results from the cognitive engine, and consume the recommendations that improve business metrics and outcomes.  Will data scientists still have a role in organizations?  Yes, but their focus will most likely stay rooted in cutting edge analytic discovery and innovation labs, not moved into production support.

6) Cognitive systems have to be an opaque box.  Measures and metrics to benchmarks stimulate optimization and improvement.  However, a black box limits an organization's ability to learn why the system is making the decisions it makes.  Simply receiving a recommended next best action and confidence factor may be sufficient to feed into a personalized and localized business service or process, but to improve the model and optimize at a global level requires an understanding of the factors contributing to results.  For example, demonstrated that simply knowing a FICO score or that a loan applicant should be denied or approved, it provides the factors and weights as to why this occurred.  This allows a loan officer the ability to recognize that lack of a long term credit history, for example, contributed to a denial.  The power here is both to know how the business is influenced by the cognitive system, but holistic analysis of population results can provide windows into business processes and engagements that reinforce or challenge current business practices.

7) Cognitive innovation can come from unlikely sources.  Mark Sagar from the Laboratory for Animate Technologies, Aukland Bioengineering Institute demonstrated the expressive machine.   Building on his work as an animator for several block buster movies, he demonstrated the link between how he uses the way we experience our world through all our senses and they manifest in our emotions and facial expressions.  By creating a computer animated baby he showed how his attention, voice, and interaction could cause the baby to focus, smile, and cry.  In addition, he demonstrated how the baby could be taught to read by connecting images and words.  This comes from work in movie animation.  But, he also showed that by studying brain disease and strokes how animation was able to understand the connection between brain faculties and facial musle movement in order to simulate this in animated characters.  

Looking forward to day two.  You can follow the event by tracking #CogComputing.